5,216 research outputs found
3D Face tracking and gaze estimation using a monocular camera
Estimating a user’s gaze direction, one of the main novel user interaction technologies, will eventually be used for numerous applications where current methods are becoming less effective. In this paper, a new method is presented for estimating the gaze direction using Canonical Correlation Analysis (CCA), which finds a linear relationship between two datasets defining the face pose and the corresponding facial appearance changes. Afterwards, iris tracking is performed by blob detection using a 4-connected component labeling algorithm. Finally, a gaze vector is calculated based on gathered eye properties. Results obtained from datasets and real-time input confirm the robustness of this metho
Tracking Gaze and Visual Focus of Attention of People Involved in Social Interaction
The visual focus of attention (VFOA) has been recognized as a prominent
conversational cue. We are interested in estimating and tracking the VFOAs
associated with multi-party social interactions. We note that in this type of
situations the participants either look at each other or at an object of
interest; therefore their eyes are not always visible. Consequently both gaze
and VFOA estimation cannot be based on eye detection and tracking. We propose a
method that exploits the correlation between eye gaze and head movements. Both
VFOA and gaze are modeled as latent variables in a Bayesian switching
state-space model. The proposed formulation leads to a tractable learning
procedure and to an efficient algorithm that simultaneously tracks gaze and
visual focus. The method is tested and benchmarked using two publicly available
datasets that contain typical multi-party human-robot and human-human
interactions.Comment: 15 pages, 8 figures, 6 table
Fast and Accurate Algorithm for Eye Localization for Gaze Tracking in Low Resolution Images
Iris centre localization in low-resolution visible images is a challenging
problem in computer vision community due to noise, shadows, occlusions, pose
variations, eye blinks, etc. This paper proposes an efficient method for
determining iris centre in low-resolution images in the visible spectrum. Even
low-cost consumer-grade webcams can be used for gaze tracking without any
additional hardware. A two-stage algorithm is proposed for iris centre
localization. The proposed method uses geometrical characteristics of the eye.
In the first stage, a fast convolution based approach is used for obtaining the
coarse location of iris centre (IC). The IC location is further refined in the
second stage using boundary tracing and ellipse fitting. The algorithm has been
evaluated in public databases like BioID, Gi4E and is found to outperform the
state of the art methods.Comment: 12 pages, 10 figures, IET Computer Vision, 201
Human-centric light sensing and estimation from RGBD images: The invisible light switch
Lighting design in indoor environments is of primary importance for at least
two reasons: 1) people should perceive an adequate light; 2) an effective
lighting design means consistent energy saving. We present the Invisible Light
Switch (ILS) to address both aspects. ILS dynamically adjusts the room
illumination level to save energy while maintaining constant the light level
perception of the users. So the energy saving is invisible to them. Our
proposed ILS leverages a radiosity model to estimate the light level which is
perceived by a person within an indoor environment, taking into account the
person position and her/his viewing frustum (head pose). ILS may therefore dim
those luminaires, which are not seen by the user, resulting in an effective
energy saving, especially in large open offices (where light may otherwise be
ON everywhere for a single person). To quantify the system performance, we have
collected a new dataset where people wear luxmeter devices while working in
office rooms. The luxmeters measure the amount of light (in Lux) reaching the
people gaze, which we consider a proxy to their illumination level perception.
Our initial results are promising: in a room with 8 LED luminaires, the energy
consumption in a day may be reduced from 18585 to 6206 watts with ILS
(currently needing 1560 watts for operations). While doing so, the drop in
perceived lighting decreases by just 200 lux, a value considered negligible
when the original illumination level is above 1200 lux, as is normally the case
in offices
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